Longitudinal analysis within one hospital in sub-Saharan Africa over 20 years reveals repeated replacements of dominant clones of Klebsiella pneumoniae and stresses the importance to include temporal patterns for vaccine design considerations

Background Infections caused by multidrug-resistant gram-negative bacteria present a severe threat to global public health. The WHO defines drug-resistant Klebsiella pneumoniae as a priority pathogen for which alternative treatments are needed given the limited treatment options and the rapid acquisition of novel resistance mechanisms by this species. Longitudinal descriptions of genomic epidemiology of Klebsiella pneumoniae can inform management strategies but data from sub-Saharan Africa are lacking. Methods We present a longitudinal analysis of all invasive K. pneumoniae isolates from a single hospital in Blantyre, Malawi, southern Africa, from 1998 to 2020, combining clinical data with genome sequence analysis of the isolates. Results We show that after a dramatic increase in the number of infections from 2016 K. pneumoniae becomes hyperendemic, driven by an increase in neonatal infections. Genomic data show repeated waves of clonal expansion of different, often ward-restricted, lineages, suggestive of hospital-associated transmission. We describe temporal trends in resistance and surface antigens, of relevance for vaccine development. Conclusions Our data highlight a clear need for new interventions to prevent rather than treat K. pneumoniae infections in our setting. Whilst one option may be a vaccine, the majority of cases could be avoided by an increased focus on and investment in infection prevention and control measures, which would reduce all healthcare-associated infections and not just one. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-024-01342-3.


Fig S12.
Visualisation of assembly graphs as generated by flye using bandage.The assembled contigs are illustrated and show the length in bp after polishing steps as well as the plasmid replicons as predicted by the online plasmidfinder platform (A) and the predicted virulence-gene operon is shown in (B) as assembled.The virulence operon is at the contig end of the IncFI/FII plasmid and thus also misassembled into a small fragment, reflective of the challenges in assembling mobile elements.
Table S1: Accessions, metadata and kleborate predictions per strain.Table S2: Antimicrobial gene predictions using ariba.Table S3: Plasmid replicon predictions using ariba.Table S4: Phenotypic resistance profiles.Table S5: O-and K-antigen predictions using updated definitions in kaptive.Table S6: Long-read sequencing assembly description and accessions.

Fig S4.
Fig S4.Phenotypic resistance profiles of all sequenced isolates.This highlights very high resistance against widely used first-line treatments like (A) cefpodoxime (ceftriaxone; 3GC), (B) augmentin (BLBLI combination), (C) gentamicin (AGly), (D) cotrimoxazole (TMT) but still a large proportion of sensitive isolates against less commonly used antimicrobials; (E) ciprofloxacin (Fq), (F) amikacin (AGly), and a pattern of increasing sensitivity against (G) chloramphenicol.The currently only available last-line treatments are (H) piperazillintazobactam (BLBLI combination), which show very high resistance levels; and (I) meropenem (carbapenem), the currently only viable alternative tested.(J) Cefoxitin also shows a lot of sensitive isolates; this antimicrobial is routinely used in diagnostic laboratories world-wide to test for ampC production as it was rapidly replaced with third-generation cephalosporins after its initial characterisation.The resistance profile is however of high interest as clinical trials have been started recently to assess it as a potential treatment alternative.

Fig S7 .
Fig S7.Predicted resistance and virulence genes and plasmid replicons for K. variicola.The guidance tree is as shown in Fig.3C, highlighting closely related isolates by tip labels.The heatmaps show the presence (dark shade) or absence (light shade) of (A) virulence and resistance genes and (B) plasmid replicons.

Fig S11 .
Fig S11.Proportions of virulent isolates and resistant isolates over time have markedly different pattern.(A)The number of isolates with a kleborate-predicted virulence score > 1 (1 usually indicates yersiniabactin which is chromosomally fixed in several STs) which remains very low and stable over time and found sporadically across various wards, whereas (B) isolates with resistance scores 1 or over (indicating ESBL or, in the case of score of 2, carbapenemase present) rapidly increase over time and biased towards several wards.This is reflected in the proportions; whilst isolates encoding several virulence factors remain at very low proportion over time (C), we can see the rapid increase of resistant isolates which remains at a very high level since the initial increase (D).

Fig S3. Main ST isolates per wards over time. Each
panel shows the number of isolates for the relevant ST as in the legend.The number of isolates per year are stratified by the major wards as in the figure legend.

Predicted resistance and virulence genes and plasmid replicons for K. pneumoniae.
The guidance tree is as shown in Fig.3A, highlighting closely related isolates by tip labels.The heatmaps show the presence (dark shade) or absence (light shade) of (A) virulence genes, (B) resistance genes and (C) plasmid replicons.

Predicted resistance and virulence genes and plasmid replicons for K. quasipneumoniae.
The guidance tree is as shown in Fig.3B, highlighting closely related isolates by tip labels.The heatmaps show the presence (dark shade) or absence (light shade) of (A) virulence and resistance genes and (B) plasmid replicons.